Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds
| dc.contributor.author | Chow, Hsiao-Hui | * |
| dc.contributor.author | Chen, Hsinchun | * |
| dc.contributor.author | Ng, Tobun Dorbin | * |
| dc.contributor.author | Myrdal, P. | * |
| dc.contributor.author | Yalkowsky, S.H. | * |
| dc.date.accessioned | 2004-10-13T00:00:01Z | |
| dc.date.available | 2010-06-18T23:34:33Z | |
| dc.date.issued | 1995-07 | en_US |
| dc.date.submitted | 2004-10-13 | en_US |
| dc.identifier.citation | Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds 1995-07, 35(4):723-728 Journal of Chemical Information and Computer Sciences, American Chemical Society | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105793 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.abstract | This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented structural information. A set of 95 compounds was used to train the neural network, and the trained network was tested on a set of 31 compounds. A comparison was made between the results and those obtained using multiple linear regression analysis. With the proper selection of neural network parameters, the backpropagation network provided a more accurate prediction of the aqueous activity coefficients for testing data than did regression analysis. This research indicates that neural networks have the potential to become a useful analytical technique for quantitative prediction of structure-activity relationships. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Geographic Information Science | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.title | Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | Journal of Chemical Information and Computer Sciences, American Chemical Society | en_US |
| refterms.dateFOA | 2018-04-26T18:13:23Z | |
| html.description.abstract | This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented structural information. A set of 95 compounds was used to train the neural network, and the trained network was tested on a set of 31 compounds. A comparison was made between the results and those obtained using multiple linear regression analysis. With the proper selection of neural network parameters, the backpropagation network provided a more accurate prediction of the aqueous activity coefficients for testing data than did regression analysis. This research indicates that neural networks have the potential to become a useful analytical technique for quantitative prediction of structure-activity relationships. |
